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2024 International conference on Intelligent Robots and systems

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen Purdue Univ Flight Dynam & Control Hybrid Syst Lab W Lafayette IN 47907 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Buffalo Dept Comp Sci & Engn Spatial AI & Robot SAIR Lab Buffalo NY 14260 USA

ISBN: (纸本)9798350377712;9798350377705

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20x faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents).

关键词： Traveling salesman problem

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data-driven control of nonlinear systems from input-output data 62

Data-driven control of nonlinear systems from input-output d...

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62nd ieee conference on Decision and control (CDC)

作者： Dai, X. De Persis, C. Monshizadeh, N. Tesi, P. Univ Groningen Inst Engn & Technol Groningen NL-9747 AG Groningen Netherlands Univ Florence Dept Informat Engn I-50139 Florence Italy

ISBN: (纸本)9798350301243

The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "learning controllers from data via approximate nonlinearity cancellation," ieee Transactions on Automatic control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.

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Reinforcement learning for FACTS Setpoint control with Limited Information

Reinforcement Learning for FACTS Setpoint Control with Limit...

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2024 ieee PES Innovative Smart Grid Technologies Europe

作者： Tarle, Magnus Larsson, Mats Ingestrom, Gunnar Bjorkman, Marten KTH Stockholm Sweden Hitachi Energy Baden Switzerland Hitachi Energy Vasteras Sweden

ISBN: (纸本)9798350390438;9798350390421

A coordinated control of Flexible AC Transmission systems (FACTS) reference setpoints is often absent in real systems. Despite the power quality gains demonstrated in studies, this absence can partly be derived from challenges with model-based control. As promising alternative methods of control, data driven approaches based on reinforcement learning (RL) have been considered. In this work, we study the potential gains in power quality using RL while recognizing the increasing number of installed Phasor Measurement Units, providing limited but reliable information. We demonstrate on the ieee 14-bus and ieee 57-bus systems that by adding a few measurements per FACTS device and a constraint violation signal, an RL scheme may significantly improve power quality compared to a baseline of fixed setpoints. To evaluate robustness, several configurations are simulated and for larger systems, we identify unobserved constraint violations as the main risk and propose a potential path for new research.

关键词： Decision support systems Flexible AC Transmission systems (FACTS) power system control reinforcement learning

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A Comprehensive Machine learning Methodology for Embedded systems PHM 22

A Comprehensive Machine Learning Methodology for Embedded Sy...

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ieee 22nd International conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Pimentel, Juliano McEwan, Alistair A. Yu, Hong Qing Univ Derby Sch Comp & Engn Derby DE22 1GB England

ISBN: (纸本)9798350381993;9798350382006

Techniques of Machine learning (ML) have recently been widely used in several applications, but not much for embedded systems, particularly those of safety-critical functionality, and are expected to help solve complex PHM and RUL which are difficult to be overcome with traditional approaches. This paper proposes a novel approach to encompass data-driven system-level health management, which comprises three main processes: an exhaustive offline training using machine learning techniques to derive a model capable of predicting the system of interest within fixed performance criteria;followed by its deployment on the targeted embedded system for real-time health assessment and finally the ability to provide a multi-step ahead forecasting for the system prognostics. The ability to monitor the current state of the systems and predict their behaviour becomes essential. Therefore, techniques to establish fault tolerance and fault prediction are required. Condition monitoring (CM), prognostics and health management (PHM) and remaining useful life (RUL) are enablers for fault detection, fault progression and system degradation. This paper presents a ML-based methodology for the PHM of embedded systems, aiming at generalization and real-time performance.

关键词： PHM Machine learning Embedded systems Real-Time Condition Monitoring Remaining Useful Life Fault Taxonomy

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learning-Aided Kalman Tracking in Biased Dynamic systems: The Case of Cable-driven Robots for Surgery

Learning-Aided Kalman Tracking in Biased Dynamic Systems: Th...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ketashvili, Linoy Ashkenasy, Shachar Nisky, Ilana Shlezinger, Nir School of ECE Ben-Gurion University of the Negev Beer-Sheva Israel Department of BME Ben-Gurion University of the Negev Israel

ISBN: (纸本)9798350368741

In cable-driven robots, the actuation is transmitted via long cables for control and manipulation of the end-effectors. The cables introduce biases due to non-linear tension, creating a challenge for accurate modeling and localization. This paper presents a novel data-driven algorithm that addresses these biases in partially known dynamic systems, focusing on cable-driven robots. The proposed approach integrates classic Kalman filtering with deep learning to enhance tracking and overcome the limitations of existing state-space modeling. Our algorithm learns from data to explicitly track the robot's end-effector, while implicitly tracking its cable-induced bias. We demonstrate the effectiveness of the algorithm on two different robotic manipulators: a planar two-link robotic manipulator, and the Raven surgical robot, constituting testbeds for dynamic systems where the bias arises from time-varying cable tension. Our experimental results reveal that while conventional methods like the Extended Kalman Filter struggle with pose tracking due to system biases, our algorithm successfully localizes the robot's end-effector and uncovers underlying biases, showing significant advancements in predictive capabilities and dynamic system understanding. © 2025 ieee.

关键词： Cable-driven robots Kalman filter

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data-driven Designs of Fault Identification via Collaborative Deep learning for Traction systems in High-Speed Trains

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ieee TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2022年第2期8卷 1748-1757页

作者： Cheng, Chao Wang, Weijun Ran, Guangtao Chen, Hongtian Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

Due to the advanced development of sensor technology, the data deluge has begun in the complex systems of high-speed trains (HSTs) and, therefore, hastens the popularity of data-driven research. Among these activities, data-driven detection and identification of faults have received considerable attention to ensure the safe and reliable operations of HST, especially the deep learning-based methods. Up to now, these deep learning-based methods are effective only for static systems. It, hence, motivates us to develop the data-driven fault identification (FI) method for traction systems in HST. In this study, we will develop an FI method via the collaborative deep learning method, where the first neural network is used for eliminating dynamic behaviors, and the second neural network is responsible for identifying the fault amplitude. By the use of the proposed neural networks with a deep architecture, the FI task can be achieved in a collaborative fashion. Its successful application on the traction systems of HST illustrates the effectiveness of collaborative deep learning on the one hand and opens an avenue on the data-driven FI methods using neural networks on the other hand.

关键词： Transportation Collaborative learning data-driven designs fault identification (FI) neural networks

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SG-DLCT: Saliency-Guided Dual-Level Collaborative Transformer for Image Captioning 13

SG-DLCT: Saliency-Guided Dual-Level Collaborative Transforme...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, Xinchao Deng, Jingzheng Zhang, Kanjian Zhang, Jinxia Southeast University Shenzhen Research Institute Shenzhen518057 China School of Automation Southeast University The Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350361674

Improving the generation of feature representations for images has been a central focus in the field of image captioning. However, existing approaches, whether based on grid features or region features, only consider the features within individual regions without capturing the interactions between objects. In this paper, we propose a novel Saliency-Guided Dual-Level Collaborative Transformer (SG-DLCT), which leverages saliency information to guide the network in capturing the importance of different objects and their interrelationships. Concretely, we utilize a pre-trained RGBD salient object ranking model to obtain saliency information. Building upon this, we propose the Saliency Feature Enhanced Module (SFEM), which optimizes region features using saliency values as weights, thereby guiding the model to focus on image regions that are more meaningful for generating image captions. Additionally, we propose the Saliency-Guided Cross Attention (SGCA), which leverages saliency information to model the interaction between region features and grid features, allowing the final model's features to benefit from the advantages of both types of features. Experimental results validate the significance of saliency information in image captioning tasks and demonstrate the effectiveness of our proposed method. © 2024 ieee.

关键词： Image Captioning Salient Object Ranking Transformer

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Graph-Structured Super-Resolution for Geometry- Generalized Tomographic Tactile Sensing: Application to Humanoid Faces

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ieee TRANSACTIONS ON ROBOTICS 2025年 41卷 558-572页

作者： Park, Hyunkyu Kim, Woojong Jeon, Sangha Na, Youngjin Kim, Jung Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Mech Syst Engn Seoul 04310 South Korea

Electrical impedance tomographic (EIT) tactile sensing holds great promise for whole-body coverage of contact-rich robotic systems, offering extensive flexibility in sensor geometry. However, low spatial resolution restricts its practical use, despite the existing deep-learning-based reconstruction methods. This study introduces EIT-GNN, a graph-structured data-driven EIT reconstruction framework that achieves super-resolution in large-area tactile perception on unbounded form factors of robots. EIT-GNN represents the arbitrary sensor shape into mesh connections, then employs a twofold architecture of transformer encoder and graph convolutional neural network to best manage such the geometrical prior knowledge, resulting in the accurate, generalized, and parameter-efficient reconstruction procedure. As a proof-of-concept, we demonstrate its application using large-area face-shaped sensor hardware, which represents one of the most complex geometries in human/humanoid anatomy. An extensive set of experiments, including simulation study, ablation analysis, single-touch indentation test, and latent feature analysis, confirm its superiority over alternative models. The beneficial features of the approach are demonstrated through its application in active tactile-servo control of humanoid head motion, paving the new way for integrating tactile sensors with intricate designs into robotic systems.

关键词： Geometry Sensors Tactile sensors Image reconstruction Electrical impedance tomography Robots Shape Hardware Faces Calibration Deep learning in robotics and automation force and tactile sensing sensor-based control tomographic reconstruction

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Multi-Scale Boundary Perception Cascaded Segmentation Network for Pancreatic Lesione 13

Multi-Scale Boundary Perception Cascaded Segmentation Networ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Chen, Jiashu Ren, Qiubing Yu, Yulin Li, Sheng He, Xiongxiong College of Control Science and Engineering Zhejiang University Hangzhou310007 China College of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou310023 China

ISBN: (纸本)9798350361674

The segmentation of pancreatic lesions in CT images is crucial for clinical diagnosis and treatment. Nevertheless, accurately segmenting pancreatic lesions presents a challenge owing to the low contrast between the pancreas and surrounding tissues, the diverse anatomical structures of the pancreas and its lesions, and the inherent issue of blurred boundaries. This paper proposes a multi-scale boundary perception cascaded network (MBPC) for the segmentation of pancreatic lesions in CT images. To address the challenge of low contrast between the pancreas and surrounding tissues, we have developed an effective preprocessing scheme. The encoder features a multi-scale fusion module, facilitating the extraction of semantic information at different scales and mitigating the impact of shape transformations. Then, We introduced the Boundary-Aware Module (BAM) and the Boundary Fusion Module (BFM) to extract boundary information for the pancreas and its lesions, thereby improving the model's performance in addressing edge-related tasks within the pancreatic and lesion regions. Finally, the proposed method is experimentally validated on public datasets, demonstrating competitive performance compared to some state-of-the-art pancreatic segmentation algorithms. © 2024 ieee.

关键词： Computerized tomography

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Synergistic Reinforcement and Imitation learning for Vision-driven Autonomous Flight of UAV Along River

Synergistic Reinforcement and Imitation Learning for Vision-...

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2024 International conference on Intelligent Robots and systems

作者： Wang, Zihan Li, Jianwen Mahmoudian, Nina Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350377712;9798350377705

Vision-driven autonomous flight and obstacle avoidance of Unmanned Aerial Vehicles (UAVs) along complex riverine environments for tasks like rescue and surveillance requires a robust navigation policy, which is yet difficult to obtain due to the shortage of trainable riverine environment simulators. To easily verify the vision-based navigation controller performance for the river following task before real-world deployment, we developed a trainable photo-realistic dynamics-free riverine simulation environment using Unity. In this paper, we address the shortcomings that vanilla Reinforcement learning (RL) algorithm encounters in learning a navigation policy within this partially observable, non-Markovian environment. We propose a synergistic approach that integrates RL and Imitation learning (IL). Initially, an IL expert is trained on manually collected demonstrations, which then guides the RL policy training process. Concurrently, experiences generated by the RL agent are utilized to re-train the IL expert, enhancing its ability to generalize to unseen data. By leveraging the strengths of both RL and IL, this framework achieves a faster convergence rate and higher performance compared to pure RL, pure IL, and RL combined with static IL algorithms. The results validate the efficacy of the proposed method in terms of both task completion and efficiency. The code and trainable environments are available(1).

关键词： Unmanned aerial vehicles (UAV)

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